SOTAVerified

Denoising

Denoising is a task in image processing and computer vision that aims to remove or reduce noise from an image. Noise can be introduced into an image due to various reasons, such as camera sensor limitations, lighting conditions, and compression artifacts. The goal of denoising is to recover the original image, which is considered to be noise-free, from a noisy observation.

( Image credit: Beyond a Gaussian Denoiser )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 5601–5625 of 7282 papers

TitleStatusHype
Burger: Robust Graph Denoising-augmentation Fusion and Multi-semantic Modeling in Social Recommendation—0
Sketch and Text Guided Diffusion Model for Colored Point Cloud Generation—0
Burst Denoising via Temporally Shifted Wavelet Transforms—0
SketchFusion: Learning Universal Sketch Features through Fusing Foundation Models—0
Sketch-Guided Text-to-Image Diffusion Models—0
Skip-Attention: Improving Vision Transformers by Paying Less Attention—0
An Efficient Statistical Method for Image Noise Level Estimation—0
Skrr: Skip and Re-use Text Encoder Layers for Memory Efficient Text-to-Image Generation—0
Cache Me if You Can: Accelerating Diffusion Models through Block Caching—0
CaDM: Codec-aware Diffusion Modeling for Neural-enhanced Video Streaming—0
CAGE: Controllable Articulation GEneration—0
Calibration of depth cameras using denoised depth images—0
Calliffusion: Chinese Calligraphy Generation and Style Transfer with Diffusion Modeling—0
CameraNet: A Two-Stage Framework for Effective Camera ISP Learning—0
SKYNET: an efficient and robust neural network training tool for machine learning in astronomy—0
CANDID: Correspondence AligNment for Deep-burst Image Denoising—0
Can Diffusion Models Learn Hidden Inter-Feature Rules Behind Images?—0
Can fully convolutional networks perform well for general image restoration problems?—0
A Bayesian traction force microscopy method with automated denoising in a user-friendly software package—0
Degradation-Noise-Aware Deep Unfolding Transformer for Hyperspectral Image Denoising—0
Canonical Polyadic Decomposition and Deep Learning for Machine Fault Detection—0
Can Shape-Infused Joint Embeddings Improve Image-Conditioned 3D Diffusion?—0
SLCGC: A lightweight Self-supervised Low-pass Contrastive Graph Clustering Network for Hyperspectral Images—0
GeoPos: A Minimal Positional Encoding for Enhanced Fine-Grained Details in Image Synthesis Using Convolutional Neural Networks—0
An Efficient and Robust Method for Chest X-Ray Rib Suppression that Improves Pulmonary Abnormality Diagnosis—0
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All datasetsDarmstadt Noise DatasetDIV2KDNDiris

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SINDyPSNR81—Unverified
2Pixel-shuffling DownsamplingPSNR38.4—Unverified
3TWSCPSNR37.93—Unverified
4CBDNet(Syn)PSNR37.57—Unverified
5MCWNNMPSNR37.38—Unverified
6Han et alPSNR35.95—Unverified
7FFDNetPSNR34.4—Unverified
8TNRDPSNR33.65—Unverified
9CDnCNN-BPSNR32.43—Unverified
10NLRNPSNR30.8—Unverified
#ModelMetricClaimedVerifiedStatus
1DRUnet_Poisson_0.01Average PSNR (dB)33.92—Unverified
#ModelMetricClaimedVerifiedStatus
1DRANetAverage PSNR39.64—Unverified
#ModelMetricClaimedVerifiedStatus
1PCNN+RL+HMEAverage84.61—Unverified